Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities

ABSTRACT The aim was to establish which postural and physical activity outcomes are comparable across different accelerometer brands worn on the thigh when processed using open-source methods. Twenty participants wore four accelerometers (Axivity, ActiGraph, activPAL, GENEActiv) for three free-living days. Postural and physical activity outputs (average acceleration, intensity gradient, intensity of the most active 30 min, 60 min, and 8 h) were generated. Postural outputs: Mean absolute percent errors (MAPEs) were low, reliability excellent, and equivalency within the 5% zone across all monitor pairings for sitting/lying and upright times, but not specific lying postures. Physical activity outputs: MAPEs were higher and reliability lower than for sitting/lying and upright time. However, the majority of the outcomes were within the 10% equivalency zone for Axivity/GENEActiv and Axivity/ActiGraph pairings. Total sitting/lying and upright times show strong potential for harmonization across studies utilizing different thigh-worn accelerometers. The majority of acceleration outcomes compare well for Axivity, GENEActiv, and ActiGraph.

[1]  T. Vasankari,et al.  Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD) , 2015, PloS one.

[2]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[3]  U. Ekelund,et al.  Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults , 2019, Scientific Reports.

[4]  KAMLESH KHUNTI,et al.  Beyond Cut Points: Accelerometer Metrics that Capture the Physical Activity Profile , 2018, Medicine and science in sports and exercise.

[5]  Are patients with knee osteoarthritis and patients with knee joint replacement as physically active as healthy persons? , 2018, Journal of orthopaedic translation.

[6]  Pascal Madeleine,et al.  The DPhacto cohort: An overview of technically measured physical activity at work and leisure in blue-collar sectors for practitioners and researchers. , 2019, Applied ergonomics.

[7]  M. Kangas,et al.  Accelerometry-Based Characteristics of Overall Sedentary Behavior and Sitting in Middle-Aged Adults , 2019, Measurement in Physical Education and Exercise Science.

[8]  P. Dall,et al.  Validity and reliability of the activPAL3 for measuring posture and stepping in adults and young people. , 2016, Gait & posture.

[9]  Alex V Rowlands,et al.  Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being , 2019, Journal of sports sciences.

[10]  Roger G Eston,et al.  Comparability of measured acceleration from accelerometry-based activity monitors. , 2015, Medicine and science in sports and exercise.

[11]  John Staudenmayer,et al.  The activPAL TM Accurately Classifies Activity Intensity Categories in Healthy Adults. , 2016, Medicine and science in sports and exercise.

[12]  Joss Langford,et al.  Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere. , 2014, Medicine and science in sports and exercise.

[13]  Rosie Arthur,et al.  The use of the intensity gradient and average acceleration metrics to explore associations with BMI z-score in children , 2019, Journal of sports sciences.

[14]  K. Khunti,et al.  Providing a Basis for Harmonization of Accelerometer-Assessed Physical Activity Outcomes Across Epidemiological Datasets , 2019, Journal for the Measurement of Physical Behaviour.

[15]  Kamlesh Khunti,et al.  Accuracy of Posture Allocation Algorithms for Thigh- and Waist-Worn Accelerometers. , 2016, Medicine and science in sports and exercise.

[16]  James M. Pivarnik,et al.  Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior , 2016, AIMS public health.

[17]  C. Morse,et al.  Using isotemporal substitution to predict the effects of changing physical behaviour on older adults’ cardio-metabolic profiles , 2019, PloS one.

[18]  KAMLESH KHUNTI,et al.  Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent? , 2017, Medicine and science in sports and exercise.

[19]  S. Chastin,et al.  Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) , 2019, British Journal of Sports Medicine.

[20]  Alexander Horsch,et al.  Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity , 2013, PloS one.

[21]  T. Vasankari,et al.  A universal, accurate intensity‐based classification of different physical activities using raw data of accelerometer , 2015, Clinical physiology and functional imaging.

[22]  Dinesh John,et al.  An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing. , 2019, Journal for the measurement of physical behaviour.

[23]  P. Mork,et al.  Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: a proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) , 2019, International Journal of Behavioral Nutrition and Physical Activity.

[24]  Nicole A. Lazar,et al.  Testing Statistical Hypotheses of Equivalence , 2003, Technometrics.

[25]  James M. Pivarnik,et al.  Comparison of Activity Type Classification Accuracy from Accelerometers Worn on the Hip, Wrists, and Thigh in Young, Apparently Healthy Adults , 2016 .

[26]  Harri Sievänen,et al.  Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand , 2015, BMC Sports Science, Medicine and Rehabilitation.

[27]  Patty S. Freedson,et al.  Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors , 2013, Sensors.

[28]  John Staudenmayer,et al.  The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults , 2017, Medicine and science in sports and exercise.

[29]  Kamlesh Khunti,et al.  Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics , 2019, Sports Medicine - Open.

[30]  Kamlesh Khunti,et al.  Wrist-Worn Accelerometer-Brand Independent Posture Classification. , 2016, Medicine and science in sports and exercise.

[31]  Christi Deaton,et al.  Using Accelerometers to Measure Physical Activity in Older Patients Admitted to Hospital , 2018, Current gerontology and geriatrics research.

[32]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[33]  N. Owen,et al.  Identifying adults’ valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol , 2016, Physiological measurement.

[34]  L. Boddy,et al.  Validating the Sedentary Sphere method in children: Does wrist or accelerometer brand matter? , 2019, Journal of sports sciences.

[35]  A. Ojoawo,et al.  patients with knee osteoarthritis: A , 2016 .

[36]  Kamlesh Khunti,et al.  A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance. , 2019, Journal of science and medicine in sport.

[37]  Nils Y. Hammerla,et al.  Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study , 2017, PloS one.

[38]  Joss Langford,et al.  Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents , 2014, Journal of applied physiology.

[39]  Alex V. Rowlands,et al.  GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data , 2019, Journal for the Measurement of Physical Behaviour.